Intraoperative FoCUS: Training Practices and Views on Feasibility.

OBJECTIVES: To investigate whether resident anesthesiologists perceive intraoperative focused cardiac ultrasonography (FoCUS) as feasible, the self-reported confidence of residents performing intraoperative FoCUS, and United States graduate medical education resident ultrasound training practices. DESIGN: A cross-sectional survey. SETTING: The United States Accreditation Council for Graduate Medical Education-listed anesthesiology programs over a 3-month period between June 2022 to September 2022. PARTICIPANTS: United States anesthesiology residents. INTERVENTIONS: A survey. MEASUREMENTS AND MAIN RESULTS: Reported training practices were as follows: 87.3% of respondents reported formal FoCUS training, and the most commonly reported training was "lectures + live-model training under 5 hours annually" at 31%. Most respondents (82%) stated that faculty never or rarely performed bedside FoCUS, and most respondents (69%) reported no intraoperative FoCUS education exposure. The proportion of residents who reported a positive view on the perceived feasibility of intraoperative FoCUS was 53.2% for extremity surgery, 19.8% for laparoscopic surgery, 18.6% for exploratory laparotomy surgery, and 7.9% for robotic surgery. Most respondents (78.6%) indicated a lack of confidence in performing intraoperative FoCUS independently. The authors found no statistical difference in views on feasibility or reported confidence independently performing FoCUS across training years. Training that included "lectures + simulation" or "live-model" for more than 5 hours annually, faculty routinely using bedside FoCUS, and frequent exposure to intraoperative FoCUS increased the odds of reporting confidence. CONCLUSIONS: The misconception that intraoperative FoCUS is infeasible appears prevalent, and most of the authors' respondents expressed a lack of comfort independently performing intraoperative FoCUS. Alterations of training practices, including increasing faculty usage of bedside ultrasonography, increasing trainee time performing FoCUS, and incorporating specific intraoperative ultrasound into the ultrasound curriculum, may address these deficiencies.

Improved tracking of sevoflurane anesthetic states with drug-specific machine learning models.

OBJECTIVE: The ability to monitor anesthetic states using automated approaches is expected to reduce inaccurate drug dosing and side-effects. Commercially available anesthetic state monitors perform poorly when ketamine is administered as an anesthetic-analgesic adjunct. Poor performance is likely because the models underlying these monitors are not optimized for the electroencephalogram (EEG) oscillations that are unique to the co-administration of ketamine. APPROACH: In this work, we designed two k-nearest neighbors algorithms for anesthetic state prediction. MAIN RESULTS: The first algorithm was trained only on sevoflurane EEG data, making it sevoflurane-specific. This algorithm enabled discrimination of the sevoflurane general anesthesia (GA) state from sedated and awake states (true positive rate = 0.87, [95% CI, 0.76, 0.97]). However, it did not enable discrimination of the sevoflurane-plus-ketamine GA state from sedated and awake states (true positive rate = 0.43, [0.19, 0.67]). In our second algorithm, we implemented a cross drug training paradigm by including both sevoflurane and sevoflurane-plus-ketamine EEG data in our training set. This algorithm enabled discrimination of the sevoflurane-plus-ketamine GA state from sedated and awake states (true positive rate = 0.91, [0.84, 0.98]). SIGNIFICANCE: Instead of a one-algorithm-fits-all-drugs approach to anesthetic state monitoring, our results suggest that drug-specific models are necessary to improve the performance of automated anesthetic state monitors.

Automatic Detection of General Anesthetic-States using ECG-Derived Autonomic Nervous System Features.

Electroencephalogram (EEG)-based prediction systems are used to target anesthetic-states in patients undergoing procedures with general anesthesia (GA). These systems are not widely employed in resource-limited settings because they are cost-prohibitive. Although anesthetic-drugs induce highly-structured, oscillatory neural dynamics that make EEG-based systems a principled approach for anesthetic-state monitoring, anesthetic-drugs also significantly modulate the autonomic nervous system (ANS). Because ANS dynamics can be inferred from electrocardiogram (ECG) features such as heart rate variability, it may be possible to develop an ECG-based system to infer anesthetic-states as a low-cost and practical alternative to EEG-based anesthetic-state prediction systems. In this work, we demonstrate that an ECG-based system using ANS features can be used to discriminate between non-GA and GA states in sevoflurane, with a GA F1 score of 0.834, [95% CI, 0.776, 0.892], and in sevoflurane-plus-ketamine, with a GA F1 score of 0.880 [0.815, 0.954]. With further refinement, ECG-based anesthetic-state systems could be developed as a fully automated system for anesthetic-state monitoring in resource-limited settings.

Drug-Specific Models Improve the Performance of an EEG-based Automated Brain-State Prediction System.

Maintaining anesthetic states using automated brain-state prediction systems is expected to reduce drug overdosage and associated side-effects. However, commercially available brain-state monitoring systems perform poorly on drug-class combinations. We assume that current automated brain-state prediction systems perform poorly because they do not account for brain-state dynamics that are unique to drug-class combinations. In this work, we develop a k-nearest neighbors model to test whether improvements to automated brain-state prediction of drug-class combinations are feasible. We utilize electroencephalogram data collected from human subjects who received general anesthesia with sevoflurane and general anesthesia with the drug-class combination of sevoflurane-plus-ketamine. We demonstrate improved performance predicting anesthesia-induced brain-states using drug-specific models.